Saya mencoba menerapkan hanya satu sel LSTM Konvolusional dan meneruskan tensor (1,3,128,128) di dalamnya. Saya mendapatkan kesalahan ketidakcocokan ukuran.
class ConvLSTMCell(nn.Module):
def __init__(self, input_size, input_dim, hidden_dim, kernel_size, bias):
"""
Parameters
----------
input_size: (int, int)
Height and width of input tensor as (height, width).
input_dim: int
Number of channels of input tensor.
hidden_dim: int
Number of channels of hidden state.
kernel_size: (int, int)
Size of the convolutional kernel.
bias: bool
Whether or not to add the bias.
"""
super(ConvLSTMCell, self).__init__()
self.height, self.width = input_size
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.kernel_size = kernel_size
# self.padding = kernel_size[0] // 2, kernel_size[1] // 2
self.bias = bias
self.conv = nn.Conv2d(in_channels=self.input_dim + self.hidden_dim,
out_channels=4 * self.hidden_dim,
kernel_size=self.kernel_size,
#padding=self.padding,
bias=self.bias)
def forward(self, input, prev_state):
h_prev, c_prev = prev_state
print('x: {}\nh_prev: {}\nc_prev: {}'.format(x.size(), h_prev.size(), c_prev.size()))
combined = torch.cat((input, h_prev), dim=1) # concatenate along channel axis
print('combined: {}'.format(combined.size()))
combined_conv = self.conv(combined)
print('combined_conv: {}'.format(combined_conv.size()))
cc_i, cc_f, cc_o, cc_g = torch.split(combined_conv, self.hidden_dim, dim=1)
print('cc_i: {}\ncc_f: {}\ncc_o: {}\ncc_g: {}'.format(cc_i.size(), cc_f.size(), cc_o.size(), cc_g.size()))
i = torch.sigmoid(cc_i)
f = torch.sigmoid(cc_f)
o = torch.sigmoid(cc_o)
g = torch.tanh(cc_g)
print('i: {}\nf: {}\no: {}\ng: {}'.format(i.size(), f.size(), o.size(), g.size()))
c_cur = f * c_prev + i * g
h_cur = o * F.tanh(c_cur)
print('c_cur: {}\nh_cur: {}'.format(c_cur.size(), h_cur.size()))
return h_cur, c_cur
def init_hidden(self, batch_size):
return (Variable(torch.zeros(batch_size, self.hidden_dim, self.height, self.width)),
Variable(torch.zeros(batch_size, self.hidden_dim, self.height, self.width)))
x = torch.randn(1,3,128,128)
model = ConvLSTMCell(input_size=(128,128), input_dim=3, hidden_dim=3, kernel_size=(5,5),
bias=True)
hc = model.init_hidden(batch_size=1)
if gpu:
x.cuda()
model.cuda()
hc.cuda()
out = model(x, hc)
print(out.size())
Saya mendapatkan kesalahan berikut:
x: obor.Ukuran([1, 3, 128, 128])
h_prev: obor.Ukuran([1, 3, 128, 128])
c_prev: obor.Ukuran([1, 3, 128, 128])
digabungkan: obor.Ukuran([1, 6, 128, 128])
gabungan_konv: obor.Ukuran([1, 12, 124, 124])
cc_i: obor.Ukuran([1, 3, 124, 124])
cc_f: obor.Ukuran([1, 3, 124, 124])
cc_o: obor.Ukuran([1, 3, 124, 124])
cc_g: obor.Ukuran([1, 3, 124, 124])
i: obor.Ukuran([1, 3, 124, 124])
f: obor.Ukuran([1, 3, 124, 124])
o: obor.Ukuran([1, 3, 124, 124])
g: obor.Ukuran([1, 3, 124, 124])
Traceback (panggilan terakhir terakhir):
File trial.py, baris 87, masuk
keluar = model(x, hc)
File /Users/abcde/opt/anaconda3/envs/matrix/lib/python3.7/site-packages/torch/nn/modules/module.py, baris 541, dalam panggilan
hasil = self.forward(*input, **kwargs)
File trial.py, baris 66, maju
c_cur = f * c_prev + i * g
RuntimeError: Ukuran tensor a (124) harus sesuai dengan ukuran tensor b (128) pada dimensi 3 non-tunggal
Saya ingin membangun jaringan 17 sel menggunakan ini dan saya ingin menggunakan output dari setiap sel untuk menghitung kerugian terhadap kebenaran dasar. Kebenaran dasar sebanyak 18 (3.128.128) gambar.
Bagaimana cara agar jaringan saya mengeluarkan status tersembunyi dengan ukuran yang sama?
